1. A Seminar OnMobile Camera Based Text Detection & Translation Under The Guidance Of: Prof. Gaikwad K.P. Presented By:Mr. Vivek Kumar

3. History

4. Present

5. Working

6. System Flow

7. Requirement

8. Block Diagram

9. Test & Results

10. UML diagrams

11. Applications

12. Advantages &Limitation

13. Conclusion

14. Bibliography 2

15. Introduction… Our project ‘Mobile camera based text detection and translation’ retrieves text from an images and converts it into text format, then it is translated to specified language. 3

17. In about 1965, earliest form of OCR was implemented in one of the first generation computersfor Airline Ticket stock.

18. Revolutionary in 1971, it was implemented in postal services OCR systems where reading and printing of routing bar code was done on the postal code.

19. In 1974, the modifications was done which would allow blind people to have a computer read text to them out loud.

20. In late 90’s, Webcam was used for OCR process.4

22. The image can be analyzed and translated also online.

23. Only some software companies manufactures the OCR system in mobile , having high specifications.

24. ABBYY Mobile OCR, is the leading manufacture of mobile OCR.5

26. Detect edges

27. Detect corners

28. Match with stored image file

29. Retrieve text from image

30. Translate using Google API

31. Show Result6

32. Working Diagram Fig. a: Working diagram 7

34. Edge detection

35. Image feature filtering

36. Image binarization

37. Optical character recognition

38. Text correction

39. Text translation

40. Display of translation8

41. Edge Detection 1. Convert Input image into Gray – scale image : Y = 0.299R + 0.587G + 0.114B 2. Apply Blurring on image Y . 3. Find threshold value of Y2 = 9

42. Text feature filtering: 10 After Detecting Text Area, the Extraction of the character from the image is perform For Extraction & detection of the character the Edge detection, corner detection used.

44. Memory 1 GB

45. 256 MB RAM

48. Android Mobile OS inbuilt web browser11

49. Block Diagram Text Feature Filtering Captured Image Google Translator Match Image Retrieve Text Translate Text File Library Display Output Text Fig. b: Block diagram 12

50. Example c.1 c.2 c.5 Fig c.1 Fig c.2 Fig c.3 Fig c.4 Fig c.5 c.4 c.3 Fig. c: Example 13

51. Test & Results Font : Recognition rate does not vary as font changes Font size : As the size of text varies , Recognition rate will vary i.e. if the text is of larger size then recognition rate will be greater. 14

52. Test & Results Image quality : As image quality degraded recognition rate will decrease Recognition rate of character ‘A’ , ‘B’ , ‘L’ will be higher than recognition rate of character ‘y’ , ‘u’ , ‘c’. Fig. d: Test & result 15

56. Instant recognition of texts, street and e-mail addresses, links, and telephone numbers.

57. Unknown language guideline.

58. Easy to recognize road signs scripts.18

60. No tiresome manual data entry.

61. Versatility and ease of use.

62. No database is needed19

64. Many arithmetical equations cannot be recognized correctly.

65. Mobile should be of high specifications

66. For translation of extracted text , Internet connection is required.

67. Translated text may have Grammatical mistakes20

68. Conclusion This project which we have implemented is an Android Mobile OS based application which is web based real time mobile application for real-time text extraction, recognition and translation. 21

69. Bibliography Michael Hsueh “Interactive Text Recognition and Translation on a Mobile Device “ [Technical Report No. UCB/EECS-2011-57 ] YassinM.Y.Hasan and LinaJ.Karam “Morphological Text Extraction from Images” IEEE Transaction on Image Processing Vol.9 No.11, Nov 2000 Nobuyuki Otsu, A threshold selection method from gray-level histograms. IEEE Trans.Sys.,Man., Cyber 9(1):62-66 Celine Mancas-Thillou, Bernard Gosselin, Color text extraction with selective metric based clustering. Computer Vision and Image Understanding 2007 B. Epshtein, Detecting Text in Natural Scenes with Stroke Width Transform. Image Rochester NY, pp. 1-8. Derek Ma , Qiuhau Lin, Tong Zhang “Mobile Camera Based Text Detection and Translation” – research paper WWW.wikipedia.org/optical_character_recognization 22

70. Any Question..??

71. Thank yOu..!!